The present embodiments relate to segmentation in medical imaging. Accurate and fast segmentation of anatomical structures is a fundamental task in medical image analysis, enabling real-time guidance, quantification, and processing for diagnostic and interventional procedures. Previous solutions for three-dimensional segmentation are based on machine learning driven active-shape models, front-propagation theory, Markov random field methods, or deep image-to-image regression models.
Active-shape models and front-propagation theory solutions propose parametric surface models, which are deformed to fit the boundary of the target object. Machine learning techniques leverage image databases to learn complex parametric models. The deformation is either driven by pre-trained boundary classifiers or implicitly described as a level-set. These methods may suffer from several limitations, such as suboptimal local convergence and limited scalability. For high-resolution volumetric data, the use of scanning paradigms for boundary fitting lead to significant computational challenges.
In contrast, the Markov random field methods and deep image-to-image regression models include solutions that either formulate the segmentation task as the minimization of a voxel-based energy model or an end-to-end regression process from input volume to the segmentation mask. In practice, the minimization of the underlying objective functions is tedious with the search often converging to suboptimal local solutions. The same effect can be observed when applying local boundary classifiers, which are missing essential global context.